This project analyses and correlates student performance with different attributes. Then at last, it determines most suitable algorithm from bunch of them.
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Updated
Nov 1, 2017 - Python
This project analyses and correlates student performance with different attributes. Then at last, it determines most suitable algorithm from bunch of them.
The Exploratory Data Analysis and Machine Learning Model Training for the Student Performance Data
Utilizes Pandas, Matplotlib, and NumPy to analyze grades, subjects, and study habits. Gain insights into academic performance through data analysis and visualization.
Taking part in Kaggle challenges or simply picking random datasets and working on them
Dead Simple Result Analysis for VTU Engineering Students
This project predicts students' math scores using machine learning and Flask for real-time predictions.
Statistical data analysis report on Kaggle dataset Student Performance made as a personal project.
Regression Analysis using dataset from different Industries
Project for VTU result analysis, extraction and visualisations.
An advanced machine learning project for analyzing student performance, utilizing sociodemographic indicators. Hosted on AWS Elastic Beanstalk for real-time predictions and integrated with AWS CodePipeline for continuous integration and deployment.
Öğrencilerin sınavda gösterdiği performansa göre analiz
SQL script to answer the past "365 Learning Data Challenge"
This is our Mini Project for 6th semester. In this Mini Project we are developing a new webapp in which we will be performing data visualisation, dashboard designing web development using HTML5,CSS, JavaScript for web development. We are also using tools like Power BI or Tabelue for visualisation purpose.
Developed an end-to-end machine learning project using Docker and AWS, and implemented an industrial-grade code with modular architecture. The project focused on student performance prediction, achieving high accuracy through various machine learning algorithms.
various machine learning challenges
Using regression analysis, we tested the significance of predictors (such as failures and travel time) to see if they influence the final grades of a student.
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